| Title | : | Introduction to Statistical Relational Learning |
| Author | : | |
| Rating | : | |
| ISBN | : | 0262538687 |
| ISBN-10 | : | 9780262538688 |
| Language | : | English |
| Format Type | : | Paperback |
| Number of Pages | : | 608 |
| Publication | : | First published August 31, 2007 |
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
Introduction to Statistical Relational Learning Reviews
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Obviously, this is a collection of papers on a diverse set topics, rather than a textbook on a single subject. Though, the papers are mostly written in a way that they are quite approachable without the need to study a large number of other materials to get familiar with the field. Or at least they are approachable when compared to other academic papers.
My biggest reservation to this book is more that I expected something else from the field, and more importantly, I expected a more coherent reading experience centered around a single family of methods and tasks. Also, some of the papers would benefit from being constrained to a smaller number of pages. -
for those who are interested in machine learning.
very technical introductions, but not totally unapproachable. the content is more like a collection of papers about certain topics, and very comprehensive at it too.
feel free to jump from a chapter to another, or skip some chapters, there's no continuation between chapters anyway. -
Excellent source for everything on the topic of statistical relational learning (Recommended by my AI / Machine Learning professor). Covers foundational topics, current research, and future topics as well.
